Advanced Topics in AI for Networking (Summer 2023): Difference between revisions
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==List of Papers == | ==List of Papers == | ||
1. Learning for | 1. Cosmo: Contrastive Fusion Learning with Small Data for Multimodal Human Activity Recognition [https://dl.acm.org/doi/pdf/10.1145/3495243.3560519] | ||
2. | 2. Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee [https://ieeexplore.ieee.org/document/9796960] | ||
3. CASVA: Configuration-Adaptive Streaming for Live Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796875] | 3. CASVA: Configuration-Adaptive Streaming for Live Video Analytics [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796875] | ||
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7. RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796944] | 7. RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796944] | ||
14. | 8. Deep Reinforcement Learning-Based Control Framework for Radio Access Networks [https://dl.acm.org/doi/pdf/10.1145/3495243.3558276?casa_token=IlUioU8GgjwAAAAA:ygqCMlQv28WBdO-z65UXYjJIoeVoyEiwVI00D5nw_cNW0N6aHZCEdBzqt5r2A8jGT7pYuU8xJMJGIrs] | ||
9. NeuLens: Spatial-based Dynamic Acceleration of Convolutional Neural Networks on Edge [https://dl.acm.org/doi/pdf/10.1145/3495243.3560528?casa_token=mrLc2kitFkcAAAAA:lKf6MnXcwXxhr0SrODcIX7qP7DrthKc_yp7jZ-2MYoxmnutM4lHPuYXD5DrLrBjS38S15TbVwPSD-NA] | |||
10. FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796926] | |||
11. TrojanFlow: A Neural Backdoor Attack to Deep Learning-based Network Traffic Classifiers [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796878] | |||
12. Mousika: Enable General In-Network Intelligence in Programmable Switches by Knowledge Distillation [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796936] | |||
13. Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers [https://www.usenix.org/conference/nsdi22/presentation/bhardwaj] | |||
14. Top-K Deep Video Analytics: A Probabilistic Approach [https://dl.acm.org/doi/pdf/10.1145/3448016.3452786] | |||
15. Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach [https://ieeexplore.ieee.org/document/8924682] | |||
16. Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments Using A3C Learning and Residual Recurrent Neural Networks [https://ieeexplore.ieee.org/document/9169832] | |||
17. Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing [https://ieeexplore.ieee.org/document/9492755] | |||
18. Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and Routing [https://ieeexplore.ieee.org/document/10029903] | |||
19. Pyramid: Enabling Hierarchical Neural Networks with Edge Computing [https://dl.acm.org/doi/10.1145/3485447.3511990] | |||
20. Index-aware reinforcement learning for adaptive video streaming at the wireless edge [https://dl.acm.org/doi/10.1145/3492866.3549726] | |||
==Schedule== | ==Schedule== | ||
W1: Open Talk (13.4) | W1: Open Talk (13.4) | ||
W2: | W2: Select papers and create schedule | ||
W4: Paper ID: | W4: Paper ID: | ||
W6: ... | |||
W8: ... | |||
W10: ... | |||
W12: ... | |||
W14: .. | |||
'''!! xx.xx deadline for registration on Flexnow''' | '''!! xx.xx deadline for registration on Flexnow''' | ||
Latest revision as of 08:58, 13 April 2023
Details
| Workload/ECTS Credits: | 5 ECTS |
| Module: | M.Inf.1123 |
| Lecturer: | Prof. Xiaoming Fu; Dr. Tingting Yuan; Wenfang Wu; |
| Teaching assistant: | [NA] |
| Time: | Thursday 14:00-16:00 |
Announcements
Please contact me by email: wenfang.wu@cs.uni-goettingen.de if you have any questions.
Course Overview
The purpose of this seminar is to discuss some advanced topics in computer networks. This course is a theory-oriented research seminar (5 ECTS, 2 SWS), held on a weekly base and comprises the following components:
- Weekly Presentation + Weekly Paper Reading and Discussion 40%
- Final Presentation 30%
- Final Report 30%
The material in the seminar is mainly drawn from the research literature in top journals/conferences, like ToN,TMC, TPDS, SIGCOMM, SIGMETRICS, INFOCOM, MOBICOM, MOBIHOC, WWW, CoNEXT.
Requirements
- Each participant is required to read the assigned paper before the seminar and prepare the review of the paper, which should include the following parts:
- Summary of the paper
- Pros and cons of the paper (your conclusion)
- NOTE!! Every participant should provide the paper review BEFORE the seminar (23:59 on Wedesday). => the review form is available at [Paper_Review_Form_ATCN_WS201112.doc]
- During each weekly seminar, one participant is assigned for presenting the paper (each presentation lasts for ~20 minutes) and the list of pros and cons are discussed by all the participants.
- In the middle of the semester, everyone is requested to prepare:
- Final report: Essay (5~6 pages, double columns, IEEE format) for your chosen research topic, which contains a comprehensive literature survey + a detailed discussion of some key enabling technologies
- Final presentation: each presentation lasts for ~20 minutes, plus ~10 minutes Q&A
List of Papers
1. Cosmo: Contrastive Fusion Learning with Small Data for Multimodal Human Activity Recognition [1]
2. Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee [2]
3. CASVA: Configuration-Adaptive Streaming for Live Video Analytics [3]
4. Batch Adaptative Streaming for Video Analytics [4]
5. FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics [5]
6. AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning [6]
7. RouteNet-Erlang: A Graph Neural Network for Network Performance Evaluation [7]
8. Deep Reinforcement Learning-Based Control Framework for Radio Access Networks [8]
9. NeuLens: Spatial-based Dynamic Acceleration of Convolutional Neural Networks on Edge [9]
10. FeCo: Boosting Intrusion Detection Capability in IoT Networks via Contrastive Learning [10]
11. TrojanFlow: A Neural Backdoor Attack to Deep Learning-based Network Traffic Classifiers [11]
12. Mousika: Enable General In-Network Intelligence in Programmable Switches by Knowledge Distillation [12]
13. Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers [13]
14. Top-K Deep Video Analytics: A Probabilistic Approach [14]
15. Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach [15]
16. Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments Using A3C Learning and Residual Recurrent Neural Networks [16]
17. Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing [17]
18. Leveraging Deep Reinforcement Learning With Attention Mechanism for Virtual Network Function Placement and Routing [18]
19. Pyramid: Enabling Hierarchical Neural Networks with Edge Computing [19]
20. Index-aware reinforcement learning for adaptive video streaming at the wireless edge [20]
Schedule
W1: Open Talk (13.4)
W2: Select papers and create schedule
W4: Paper ID:
W6: ...
W8: ...
W10: ...
W12: ...
W14: ..
!! xx.xx deadline for registration on Flexnow
Final Presentation (xx.07)
- Paper Title:
- Paper Title:
Report deadline
Final Presentations & Report
- Final Registration in FlexNow: To Be Announced (TBA).
- Final Presentation:
- Each for ~20 minutes, plus ~10 minutes Q&A
- Final Presentation Slots:
- To Be Announced (TBA)
- Final Report:
- Essay (~6 pages, double column, IEEE format: https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-journal-article/authoring-tools-and-templates/ieee-article-templates/templates-for-transactions/)
- Due by 23:59pm 15th August.